Intrinsically disordered regions lack stable structure in their native conformation but are nevertheless functional and highly abundant, particularly in Eukaryotes. Disordered moonlighting regions (DMRs) are intrinsically disordered regions that carry out multiple functions. DMRs are different from moonlighting proteins that could be structured and that are annotated at the whole‐protein level. DMRs cannot be identified by current predictors of functions of disorder that focus on specific functions rather than multifunctional regions. We conceptualized, designed and empirically assessed first‐of‐its‐kind sequence‐based predictor of DMRs, DMRpred. This computational tool outputs propensity for being in a DMR for each residue in an input protein sequence. We developed novel amino acid indices that quantify propensities for functions relevant to DMRs and used evolutionary conservation, putative solvent accessibility and intrinsic disorder derived from the input sequence to build a rich profile that is suitable to accurately predict DMRs. We processed this profile to derive innovative features that we input into a Random Forest model to generate the predictions. Empirical assessment shows that DMRpred generates accurate predictions with area under receiver operating characteristic curve = 0.86 and accuracy = 82%. These results are significantly better than the closest alternative approaches that rely on sequence alignment, evolutionary conservation and putative disorder and disorder functions. Analysis of abundance of putative DMRs in the human proteome reveals that as many as 25% of proteins may have long >30 residues) DMRs. A webserver implementation of DMRpred is available at
We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to hydrodynamic size. While our results were consistent with that idea, we also found that many different descriptors could successfully differentiate between three classes of protein regions: folded, intrinsically disordered, and phase‐separating intrinsically disordered. Consequently, numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. Built from that finding, ParSe 2.0 uses an optimal set of property scales to predict domain‐level organization and compute a sequence‐based prediction of phase separation potential. The algorithm is fast enough to scan the whole of the human proteome in minutes on a single computer and is equally or more accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation. Here, we describe a web application for ParSe 2.0 that may be accessed through a browser by visiting
- Award ID(s):
- 1818090
- NSF-PAR ID:
- 10451574
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Protein Science
- Volume:
- 32
- Issue:
- 9
- ISSN:
- 0961-8368
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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